healthiar in numbers

• 1 purpose - to help you quantify and monetize the attributable burden of disease

• 3 dimensions - health, monetization, social aspects

• 19 functions

• > 1000 calculation pathways

• 60000 (simple) assessments per second

healthiar overview

healthiar overview

healthiar overview

Refresher - Burden of disease with relative risk

Refresher - Burden of disease with relative risk

Refresher - Burden of disease with relative risk

Refresher - Burden of disease with relative risk

attribute_health() with RR 1/3

Goal: attribute COPD cases to PM2.5 air pollution exposure

results_pm_copd <- attribute_health(
  erf_shape = "log_linear", # erf = exposure-response function
  rr_central = 1.369, 
  rr_increment = 10,  # μg / m^3
  exp_central = 8.85, # μg / m^3
  cutoff_central = 5, # μg / m^3
  bhd_central = 30747 # bhd = baseline health data (here: COPD incidence)
) 

results_pm_copd <- attribute_health(
  erf_shape = "log_linear",
  rr_central = exdat_pm$relative_risk, 
  rr_lower = exdat_pm$relative_risk_lower,
  rr_upper = exdat_pm$relative_risk_upper,
  rr_increment = 10, 
  exp_central = exdat_pm$mean_concentration,
  cutoff_central = exdat_pm$cut_off_value,
  bhd_central = exdat_pm$incidence
) 

attribute_health() with RR 2/3

attribute_health() outputs two lists (“folders”)

• health_main contains the main results

• health_detailed contains more detailed results and other assessment details in sub-lists (“sub-folders”)

results_pm_copd$health_main

compare() two scenarios 2/2

results_comparison$health_main

attribute_health() across multiple geographic units 1/3

Goal: attribute disease cases to PM2.5 exposure in multiple geographic units, such as municipalities, provinces, countries, …

results_iteration <- attribute_health(
  geo_id_micro = c("Zürich", "Basel", "Geneva", "Ticino", "Jura"), 
  geo_id_macro = c("German","German","French","Italian","French"),
  erf_shape = "log_linear",
  rr_central = 1.369,
  rr_increment = 10, 
  cutoff_central = 5,
  exp_central = c(11, 11, 10, 8, 7),
  bhd_central = c(4000, 2500, 3000, 1500, 500)
)

attribute_health() across multiple geographic units 2/3

results_iteration$health_detailed$results_raw

attribute_health() across multiple geographic units 3/3

results_iteration$health_main

summarize_uncertainty() 1/4

If we add 95% confidence intervals to attribute_health() …

results_pm_copd <- attribute_health(
  erf_shape = "log_linear",
  rr_central = 1.369, rr_lower = 1.114, rr_upper = 1.664,
  rr_increment = 10,
  exp_central = 8.85, exp_lower = 8, exp_upper = 10,
  cutoff_central = 5,
  bhd_central = 30747, bhd_lower = 28000, bhd_upper = 32000
) 

results_pm_copd$health_detailed$results_raw

summarize_uncertainty() 2/4

Goal: perform a Monte Carlo simulation to obtain summary uncertainty confidence intervals that combine uncertainty in different input data

results <- 
  attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_lower = 1.114, 
    rr_upper = 1.664,
    rr_increment = 10,
    exp_central = 8.85, 
    exp_lower = 8, 
    exp_upper = 10,
    cutoff_central = 5,
    bhd_central = 30747, 
    bhd_lower = 28000, 
    bhd_upper = 32000) 

results <- 
  summarize_uncertainty(
    output_attribute = results,
    n_sim = 100
  )

summarize_uncertainty() 3/4

results$uncertainty_main

summarize_uncertainty() 4/4

results$uncertainty_detailed$impact_by_sim

attribute_health() with RR and user-defined ERF 1/2

Goal: attribute COPD cases to air pollution exposure by applying a user-defined exposure response function (e.g. MR-BRT curves from Global Burden of Disease study)

• Any function of the form intercept + a x c^1 + b x c^2 + …

• Any other (non-linear) function of the function type, as obtained from e.g. splinefun() or approxfun()

results_pm_copd_mr_brt <- attribute_health(
  exp_central = 8.85,
  bhd_central = 30747,
  erf_eq_central = splinefun(
    x = c(0, 5, 10, 15, 20, 25, 30, 50, 70, 90, 110),
    y = c(1.00, 1.04, 1.08, 1.12, 1.16, 1.20, 1.23, 1.35, 1.45, 1.53, 1.60),
    method = "natural")
)

attribute_health() with RR and user-defined ERF 2/2

attribute_health() with RR and exposure distribution

Goal: attribute COPD cases to air pollution exposure categories

results_pm_copd <- attribute_health(
  erf_shape = "log_linear",
  rr_central = 1.369, 
  rr_increment = 10,
  exp_central = c(5, 6, 7, 8, 9, 10),
  prop_pop_exp = c(0.2, 0.1, 0.2, 0.3, 0.1, 0.1),
  cutoff_central = 5,
  bhd_central = 30747 
)

results_pm_copd$health_main

Refresher - Burden of disease with absolute risk

Refresher - Burden of disease with absolute risk

attribute_health() with AR 1/2

Goal: attribute cases of high annoyance (HA) to noise exposure

results_noise_ha <- attribute_health(
  approach_risk = "absolute_risk",
  erf_eq_central = "78.9270-3.1162*c+0.0342*c^2",
  exp_central = c(57.5, 62.5, 67.5, 72.5, 77.5),
  pop_exp = c(387500, 286000, 191800, 72200, 7700),
)

attribute_health() with AR 2/2

results_noise_ha$health_main

results_noise_ha$health_detailed$results_raw

Other healthiar functions

• attribute_lifetable() for YLL
• attribute_health() for YLD (using the dw & duration arguments)
• get_daly() as the sum of YLL and YLD
• monetize() monetizes (attributable) health impacts
• cba() performs a cost-benefit analysis (CBA)
• multiexpose() considers exposure to 2 exposures (e.g. two different air pollutants) at the same time to quantify the attributable health impacts
• prepare_exposure() calculates (population-weighted) mean exposure by combining spatial exposure geographic units data
• socialize() determines burden attributable to differences in a social indicator
• attribute_mod() modifies an existing attribute_health() assessment
• prepare_mdi() creates the BEST-COST MDI (Multidimensional Deprivation Index)

Outlook

• healthiar is publicly available on this GitHub page

• Python version coming out later in 2026

• Package submitted to CRAN

• healthiar might be expanded in the future (if funding available)

Second hour programme

• Introduction of exercises (ca. 5 min)

• Time for exercises in small groups (ca. 25 min)

• Presentation of solutions in plenum (ca. 10 min)

• Q & A (remaining time, ca. 15 min)

Addtional function examples

For additional function examples please see the function documentations and the package vignette.

The next slides will show you how to access them.

healthiar in RStudio 1/3

Figure: Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.

healthiar in RStudio 2/3

Figure: Intro to healthiar vignette (= detailed guide to the package)

healthiar in RStudio 3/3

Figure: Help page of attribute_health()

Export results

write.csv(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.csv")

save(results_pm_copd, file = "exported_results/results_pm_copd.Rdata")

write.xlsx(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.xlsx")

Visualize results

Visualization is out of scope of healthiar. You can visualize in

• R, e.g. with the ggplot2 package (online book by the creator)
• Excel (export results first)
• Other tools